Scale-Aware Graph Convolutional Network for Fine-Grained Image Classification

Abstract

Fine-grained Image Classification (FGIC) is a hot research topic in computer vision. Currently, FGIC faces several challenges, such as similar appearances, cluttered backgrounds, and pose variations. To effectively address these challenges, we propose a framework called Scale-Aware Graph Convolutional Network (SAGCN) to capture subtle differences in images. Leveraging the characteristics of fine-grained images, we design two core modules, namely Scale-Aware Selection Module (SASM) and Spatial Semantic Correlation Module (SSCM). SASM aggregates multi-scale information of fine-grained images by fusing features from multiple layers. SSCM establishes semantic-spatial relationships by propagating information among different parts of the fine-grained image. Furthermore, we propose a Pairwise Appearance Similarity Loss (PAS-Loss) to distinguish easily confused categories. Extensive experiments demonstrate that our method achieves state-of-the-art results on benchmark datasets.